class_docstr: Abstract N-D transposed convolution layer.
  
  The need for transposed convolutions generally arises from the desire to use
  a transformation going in the opposite direction of a normal convolution,
  i.e., from something that has the shape of the output of some convolution to
  something that has the shape of its input while maintaining a connectivity
  pattern that is compatible with said convolution.
  
  Args:
      rank: int, the rank of the transposed convolution, e.g. 2 for 2D
          transposed convolution.
      filters: int, the dimension of the output space (the number of filters
          in the transposed convolution).
      kernel_size: int or tuple/list of `rank` integers, specifying the size
          of the transposed convolution window.
      strides: int or tuple/list of `rank` integers, specifying the stride
          length of the transposed convolution. If only one int is specified,
          the same stride size will be used for all dimensions.
          `strides > 1` is incompatible with `dilation_rate > 1`.
      padding: string, either `"valid"` or `"same"` (case-insensitive).
          `"valid"` means no padding. `"same"` results in padding evenly to
          the left/right or up/down of the input such that output has the same
          height/width dimension as the input.
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape `(batch, steps, features)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, features, steps)`. It defaults to the `image_data_format`
          value found in your Keras config file at `~/.keras/keras.json`.
          If you never set it, then it will be `"channels_last"`.
      dilation_rate: int or tuple/list of `rank` integers, specifying the
          dilation rate to use for dilated convolution. If only one int is
          specified, the same dilation rate will be used for all dimensions.
      activation: Activation function. If `None`, no activation is applied.
      use_bias: bool, if `True`, bias will be added to the output.
      kernel_initializer: Initializer for the convolution kernel. If `None`,
          the default initializer (`"glorot_uniform"`) will be used.
      bias_initializer: Initializer for the bias vector. If `None`, the
          default initializer (`"zeros"`) will be used.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      kernel_constraint: Optional projection function to be applied to the
          kernel after being updated by an `Optimizer` (e.g. used to implement
          norm constraints or value constraints for layer weights). The
          function must take as input the unprojected variable and must return
          the projected variable (which must have the same shape). Constraints
          are not safe to use when doing asynchronous distributed training.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
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code: def compute_output_shape(self, input_shape):
      return compute_conv_transpose_output_shape(
          input_shape,
          self.kernel_size,
          self.filters,
          strides=self.strides,
          padding=self.padding,
          output_padding=self.output_padding,
          data_format=self.data_format,
          dilation_rate=self.dilation_rate,
      )
  
file_path: keras/keras/layers/convolutional/base_conv_transpose.py
imports: from keras import activations
  from keras import constraints
  from keras import initializers
  from keras import ops
  from keras import regularizers
  from keras.backend import standardize_data_format
  from keras.backend.common.backend_utils import compute_conv_transpose_output_shape
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.utils.argument_validation import standardize_padding
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 4 for calling the BaseConvTranspose obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: compute_conv_transpose_output_shape